Abstract
A novel monocular visual simultaneous localization and mapping (SLAM) algorithm built on the semi-direct method is proposed to deal with some problems in complex environments, such as low-texture, moving objects and perceptual aliasing. The proposed algorithm takes advantage of direct and feature-based methods. On one hand, a direct method is used to track the camera poses and solve the feature alignment. On the other hand, ORB features in keyframes are extracted and matched for optimization and loop closure. To improve the localization accuracy in dynamic environments, a motion detection module that is robust to illumination change is adopted. In addition, for the sake of resolving the loop closure detection problem in perceptual aliasing scenes, this paper fuses the spatial information between two visual words into the bag of visual words (BoVW) model and employs an improved pyramid term frequency-inverse document frequency (TF-IDF) scoring match scheme. Experimental results prove that the proposed algorithm behaves better performance than ORB-SLAM with regard to overall accuracy and speed in complex environments.
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Younes, G., Asmar, D., Shammas, E., Zelek, J.: Keyframe-based monocular SLAM: design, survey, and future directions. Robot. Auton. Syst. 98, 67–88 (2017)
Hu, H., Sun, H., Ye, P., Jia, Q., Gao, X.: Multiple maps for the feature-based monocular SLAM system. J. Intell. Robot. Syst. 94, 389–404 (2019)
Blösch, M., Weiss, S., Scaramuzza, D., Siegwart, R.: Vision based mav navigation in unknown and unstructured environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 21–28 (2010)
Weiss, S., Achtelik, M., Lynen, S., Achtelik, M.: Monocular vision for long-term micro aerial vehicle state estimation: a compendium. J. Field Robot. 30(5), 803–831 (2013)
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for rgb-d cameras. In: IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, pp. 3748–3754 (2013)
Meilland, M., Comport, A.: On unifying key-frame and voxel-based dense visual slam at large scales. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 3677–3683 (2013)
Folkesson, J., Christensen, H.: Closing the Loop with Graphical SLAM. IEEE Trans. Robot. 23(4), 731–741 (2007)
Wen, L., Ray, J.: A pure vision-based topological SLAM system. Int. J. Robot. Res. 31(4), 403–428 (2012)
Li, B., Yang, D., Deng, L.: Visual vocabulary tree with pyramid TF-IDF scoring match scheme for loop closure detection. Acta Automat. Sin. 37(6), 665–673 (2011)
Strasdat, H., Montiel, J.M.M., Davison, A.J.: Visual SLAM: why filter? Image Vis. Comput. 30(2), 65–77 (2012)
Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: The 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan, pp. 225–234 (2007)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D: ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163(2015)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2012)
Engel, J., Sch, T., Cremers, D.: LSD-SLAM: large-Scale Direct Monocular SLAM. In: 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland, pp. 834–849 (2014)
Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), (2017)
Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semidirect monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22 (2014)
Yang, N., Wang, R., Gao, X., Cremers, D.: Challenges in monocular visual odometry: photometric calibration, motion bias, and rolling shutter effect. Robot. Auto. Lett. 3(4), 2878–2885 (2018)
Li, S., Zhang T., X., Gao, X.: Semi-direct monocular visual and visual-inertial SLAM with loop closure detection. Robot. Auton. Syst. 112, 201–210 (2019)
Lee, L.H., Civera, J.: Loosely-coupled semi-direct monocular SLAM. Robot. Auto. Lett. 4(2), 399–406 (2019)
Wang, Y., Huang, S.: Towards dense moving object segmentation based robust dense RGBD SLAM in dynamic scenarios. In: The 13th IEEE International Conference on Control Automation Robotics and Vision (ICARCV). pp. 1841–1846 (2014)
Bescos, B., Fácil, J.M., Civera, J., Neira, J.: DynaSLAM: Tracking,Mapping, and Inpainting in Dynamic Scenes. IEEE Robotics and Automation Letters, 3(4), 4076–4083 (2018)
Yu, C., Liu, Z., Liu, X., et al.: DS-SLAM: a semantic visual SLAM towards dynamic environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)
Angeli, A., Filliat, D., Doncieux, S., Meyer, J.: Fast and incremental method for loop-closure detection using bags of visual words. IEEE Trans. Robot. 24(5), 1027–1037 (2008)
Nicosevici, T., Garcia, R.: Automatic visual bag-of-words for online robot navigation and mapping. IEEE Trans. Robot. 28(4), 886–898 (2012)
Garcia-Fidalgo, E., Ortiz A.: Methods for appearance-based loop closure detection - applications to topological mapping and image mosaicking. Springer International Publishing (2018)
Bosch, A., Zisserman, A., Munoz., X.: Representing shape with a spatial pyramid kernel. In : ACM International Conference on Image and Video Retrieval. 401–408 (2007)
Mur-Artal, R., Tardos, J.D.: Orb-slam2: an open-source slam system for monocular, stereo and rgb-d cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)
Yun, K., Choi, J.Y.: Robust and fast moving object detection in a non-stationary camera via foreground probability based sampling. In: IEEE International Conference on Image Processing (2015)
Moo, Y.K., Yun, K., Kim, W., et al: Detection of moving objects with non-stationary cameras in 5.8 ms: Bringing motion detection to your mobile device. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 27–34 (2013)
Chang, H.J., Jeong, H., Choi, J.Y.: Active attentional sampling for speed-up of background subtraction. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
López-Rubio, F., López-Rubio, E.: Foreground detection for moving cameras with stochastic approximation. Pattern Recogn. Lett. 68, 161–168 (2015)
Arthur, D., Vassilvitskii, S.: K-means++: The advantages of careful seeding. Proceedings of the 18th annual ACM-SIAM symposium on Discrete algorithms. pp. 1027–1035 (2007)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision. pp. 1470–1477 (2003)
Robertson., S.: Understanding inverse document frequency: on theoretical arguments for idf. J. Doc.. 60(5), 503–520 (2004)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A Benchmark for the Evaluation of RGB-D SLAM Systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 573–580 (2012)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)
Cummins, M., Newman. P.: Appearance-only SLAM at large scale with FAB-MAP 2.0. Int. J. Robot. Res. 30(9), 1100–1123 (2010)
Labbé, M., Michaud, F.: Online global loop closure detection for large-scale multi-session graph-based slam. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 2661–2666 (2014)
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This work is supported by National Nature Science Foundation (Grant No.61573100) and NJUPT Program (Grant No. NY219123). We sincerely acknowledge constructive comments from editors and reviewers.
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Liang, Z., Wang, C. A Semi-Direct Monocular Visual SLAM Algorithm in Complex Environments. J Intell Robot Syst 101, 25 (2021). https://doi.org/10.1007/s10846-020-01297-8
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DOI: https://doi.org/10.1007/s10846-020-01297-8